Pancreatic cancers with aberrant expression of macrophage migration inhibitory factor (MIF) are particularly aggressive. To identify key signaling pathways that drive disease aggressiveness in tumors with high MIF expression, we analyzed the expression of coding and non-coding genes in high and low MIF-expressing tumors in multiple cohorts of pancreatic ductal adenocarcinoma (PDAC) patients. The key genes and pathways identified were linked to patient survival and were mechanistically, functionally and clinically characterized using cell lines, a genetically engineered mouse model and PDAC patient cohorts. Here we report evidence of a novel MIF-driven signaling pathway that inhibits the orphan nuclear receptor NR3C2, a previously undescribed tumor suppressor that impacts aggressiveness and survival in PDAC. Mechanistically, MIF upregulated miR-301b which targeted NR3C2 and suppressed its expression. PDAC tumors expressing high levels of MIF displayed elevated levels of miR-301b and reduced levels of NR3C2. Additionally, reduced levels of NR3C2 expression correlated with poorer survival in multiple independent cohorts of PDAC patients. Functional analysis showed that NR3C2 inhibited epithelial-to-mesenchymal transition and enhanced sensitivity to the gemcitabine, a chemotherapeutic drug used in PDAC standard of care. Furthermore, genetic deletion of MIF disrupted a MIF-mir-301b-NR3C2 signaling axis, reducing metastasis and prolonging survival in a genetically engineered mouse model of PDAC. Taken together, our results offer a preclinical proof-of-principle for candidate therapies to target a newly described MIF-miR-301b-NR3C2 signaling axis for PDAC management.

ABO blood group system, a well-known genetic risk factor, has clinically been demonstrated to be linked with thrombotic vascular diseases. However, the relationship between ABO blood group and coronary artery disease (CAD) is still controversial. We here performed an updated meta-analysis of the related studies and tried to elucidate the potential role of ABO blood group as a risk factor for CAD. All detectable case-control and cohort studies comparing the risk of CAD in different ABO blood groups were collected for this analysis through searching PubMed, Embase, and the Cochrane Library. Ultimately, 17 studies covering 225,810 participants were included. The combined results showed that the risk of CAD was significantly higher in blood group A (OR = 1.14, 95% CI = 1.03 to 1.26, p = 0.01) and lower in blood group O (OR = 0.85, 95% CI = 0.78 to 0.94, p = 0.0008). Even when studies merely about myocardial infarction (MI) were removed, the risk of CAD was still significantly higher in blood group A (OR = 1.05, 95% CI = 1.00 to 1.10, p = 0.03) and lower in blood group O (OR = 0.89, 95% CI = 0.85 to 0.93, p < 0.00001). This updated systematic review and meta-analysis indicated that both blood group A and non-O were the risk factors of CAD.

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework.